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vayuai

Vayuvahana Technologies Private Limited presents to you VajraV1, a state-of-the-art (SOTA) real time object detection model

https://github.com/namanmakkar/vayuai

Science Score: 44.0%

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Keywords

deyo image-classification instance-segmentation object-detection object-tracking pose-detection vajra yolo
Last synced: 6 months ago · JSON representation ·

Repository

Vayuvahana Technologies Private Limited presents to you VajraV1, a state-of-the-art (SOTA) real time object detection model

Basic Info
  • Host: GitHub
  • Owner: NamanMakkar
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.79 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Topics
deyo image-classification instance-segmentation object-detection object-tracking pose-detection vajra yolo
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Vayuvahana Technologies Private Limited Logo

Vayuvahana Technologies Private Limited VajraV1 is a state-of-the-art (SOTA) real time object detection model inspired by the YOLO model architectures. VajraV1 is a family of fast, lightweight models that can be used for a variety of tasks like object detection and tracking, instance segmentation, oriented object detection, pose detection, and image classification.

Enterprise License

To request for an Enterprise License please get in touch via Email

Performance on COCO Dataset

| Model | Size (pixels) | mAPval
50-95 | Speed
RTX 4090 TensorRT10 Latency (ms) | Params (M) | FLOPs (B) | |-------------------------------------------------------------------------------------|---------------|----------------------------|-----------------------------------------------------|------------|-----------| | VajraV1-nano-det | 640 | 41.2 | 1.4 | 3.36 | 8.2 | | VajraV1-small-det | 640 | 47.7 | 1.4 | 12.38 | 27.9 | | VajraV1-medium-det | 640 | | 1.8 | 21.15 | 75.1 | | VajraV1-large-det | 640 | | 2.4 | 25.75 | 93.1 | | VajraV1-xlarge-det | 640 | | 2.9 | 57.83 | 208.3 |

VajraV1 performance plot test dev VajraV1 performance plot val

Performance on VisDrone Dataset

| Model | size
(pixels) | mAPtest-dev
50-95 | mAPval
50-95 | Speed
RTX 4090 TensorRT10 Latency
(ms) | params
(M) | FLOPs
(B) | | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | | VajraV1-nano-det | 640 | 25.5 | 20.8 | 1.4 | 3.32 | 8.0 | | VajraV1-small-det | 640 | 27.3 | 24.3 | 1.4 | 12.36 | 27.7 | | VajraV1-medium-det | 640 | 29.7 | 27.7 | 1.8 | 21.09 | 74.8 | | VajraV1-large-det | 640 | 30.0 | 28.0 | 2.4 | 25.70 | 92.8 | | VajraV1-xlarge-det | 640 | 30.4 | 29.7 | 2.9 | 57.75 | 207.8 |

Results on COCO dataset to be published soon!

Documentation

Install Git clone the VayuAI SDK including all [requirements](https://github.com/NamanMakkar/VayuAI/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org) environment. ```bash git clone https://github.com/NamanMakkar/VayuAI.git cd VayuAI pip install . ```
Usage ### CLI Vajra can be used in the Command Line Interface with a `vajra` or `vayuvahana` or `vayuai` command: ```bash vajra predict model=vajra-v1-nano-det img_size=640 source="path/to/source.jpg" ``` ### Python Vajra can also be used directly in a Python environment, and accepts the same arguments as in the CLI example above: ```python from vajra import Vajra, VajraDEYO model = Vajra("vajra-v1-nano-det") model_vajra_deyo = VajraDEYO("vajra-deyo-v1-nano-det") train_results = model.train( data="coco8.yaml", epochs=100, img_size=640, device="cpu", weight_decay=0., ) metrics = model.val() results = model("path/to/img.jpg") results[0].show() path = model.export(format="onnx") ``` Pretrained Visdrone weights can also be used for model inference ```python from vajra import Vajra model = Vajra("visdrone-best-vajra-v1-xlarge-det.pt") results = model("path/to/img.jpg") results[0].show() path = model.export(format="engine", device=0, half=True) ```

Model Architectures

✅ VajraV1-det
✅ VajraV1-cls
✅ VajraV1-pose
✅ VajraV1-seg
✅ VajraV1-obb
✅ VajraV1-world
✅ VajraV1-DEYO-det
✅ VajraLiteV1-det
✅ VajraLiteV1-seg
✅ VajraLiteV1-obb
✅ VajraLiteV1-pose
✅ VajraLiteV1-cls
✅ VajraLiteV1-world
❌ VajraV1-DEYO-seg (Coming Soon!)
❌ VajraV1-DEYO-pose (Coming Soon!)
✅ SAM
✅ SAM2
✅ FastSAM
✅ MobileSAM
✅ EfficientNetV1
✅ EfficientNetV2
✅ VajraEffNetV1
✅ VajraEffNetV2
✅ ConvNeXtV1
✅ ConvNeXtV2
✅ ResNet
✅ ResNeSt
❌ ResNeXt (Coming Soon!)
❌ ResNetV2 (Coming Soon!)
✅ EdgeNeXt
✅ ME-NeSt
✅ VajraME-NeSt
✅ MixConvNeXt
❌ ViT (Coming Soon!)
❌ Swin (Coming Soon!)
❌ SwinV2 (Coming Soon!)

Tasks Supported

✅ detect
✅ smallobjdetect
✅ classify
✅ multilabel_classify
✅ pose
✅ obb
✅ segment
✅ world

Model Architecture Details

To be published

Acknowledgements

License

Vayuvahana Technologies Private Limited offers two licensing options:

  • AGPL-3.0 License: This is an OSI-approved open-source license for researchers for the purpose of promoting collaboration. See the LICENSE file for details.

  • Enterprise License: This license is designed for commercial use and enables integration of VayuAI software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your product requires embedding the software for commercial purposes or require access to more capable enterprise AI models in the future, reach out via Email.

Owner

  • Name: Naman Makkar
  • Login: NamanMakkar
  • Kind: user
  • Location: New York
  • Company: Cornell Tech

MEng CS @ Cornell Tech

Citation (CITATION.cff) 

cff-version: 1.2.0
title: Vayuvahana Technologies VayuAI
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Naman Balbir Singh
    family-names: Makkar
    affiliation: Vayuvahana Technologies Private Limited
    orcid: 'https://orcid.org/0009-0006-0735-0523'
repository-code: 'https://github.com/NamanMakkar/VayuAI'
license: AGPL-3.0
version: 1.0.0
date-released: "2024-27-09"

GitHub Events

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Dependencies

pyproject.toml pypi
  • matplotlib >=3.3.0
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
requirements.txt pypi
  • PyYAML ==6.0.1
  • onnx ==1.14.0
  • onnxruntime ==1.15.1
  • onnxruntime-gpu ==1.18.0
  • onnxsim ==0.4.36
  • pycocotools ==2.0.7
  • scipy ==1.13.0
  • torch ==2.0.1
  • torchvision ==0.15.2